{
  "public": {
    "netWorkError": "Network or backend service error. Please check.",
    "browserWarning": "Your browser may cause some functions to become invalid or unavailable. You are advised to use Chrome 65 or later.",
    "timeout": "Timeout. Try again.",
    "noData": "No data",
    "reset": "Reset",
    "tagFilterPlaceHolder": "Enter tag (regular expression supported)",
    "dataError": "The obtained data is abnormal.",
    "regIllegal": "Enter a correct search criterion.",
    "stayTuned": "Coming soon...",
    "select": "Select",
    "search": "Search",
    "enter": "Enter",
    "remark": "Remarks",
    "zhLanguage": "中",
    "enLanguage": "EN",
    "sure": "OK",
    "clear": "Clear",
    "cancel": "Cancel",
    "selectAll": "All",
    "deselectAll": "Clear",
    "dataLoading": "Loading data..."
  },
  "symbols": {
    "leftbracket": "(",
    "rightbracket": ")",
    "point": "·",
    "slashes": "/"
  },
  "header": {
    "refreshData": "Refresh Data",
    "refreshingData": "Refreshing data...",
    "timeReload": "Auto Refresh",
    "timeSecond": "Seconds",
    "timeReloadScope": "The automatic refresh interval ranges from 3 to 300 seconds."
  },
  "summaryManage": {
    "summaryList": "Summary List",
    "currentFolder": "Current folder:",
    "sorting": "No.",
    "summaryPath": "Summary Path",
    "createTime": "Creation Time",
    "updateTime": "Update Time",
    "operation": "Operation",
    "viewDashboard": "Training Dashboard",
    "viewProfiler": "Profiling",
    "modelTraceback": "Model Lineage",
    "dataTraceback": "Dataset Lineage",
    "comparePlate": "Comparison Dashboard",
    "disableProfilerTip": "Failed to view profiling because no profiler log is available.",
    "hardwareVisual": "Hardware Resources"
  },
  "modelTraceback": {
    "summaryPath": "Summary Path",
    "trainSetPath": "Training Set Path",
    "testSetPath": "Test Set Path",
    "trainingSampleNum": "Training Samples",
    "testSampleNum": "Testing Samples",
    "showAllData": "Display All Data",
    "network": "Network",
    "optimizer": "Optimizer",
    "lossFunc": "Loss Function",
    "learningRate": "Learning Rate",
    "modelSize": "Model Size",
    "dataProcess": "Data Processing",
    "noDataFound": "No data found.",
    "click": "Click",
    "showAllDataBtn": "Display All Data",
    "viewAllData": "to view all data.",
    "userDefined": "User-defined Data",
    "metric": "Metrics",
    "deviceNum": "Devices",
    "mixedItemMessage": "This parameter contains multiple types of data and cannot be filtered.",
    "displayColumn": "Displayed columns",
    "hide": "Hide Record",
    "unhide": "Unhide Record",
    "totalHide": "Hidden records on this page: {n}",
    "mustExist": "Mandatory",
    "remarkValidation": "Remarks can contain 1 to 128 characters, including letters, digits, underscores (_), hyphens (-), and periods (.).",
    "changeSuccess": "Modified successfully.",
    "metricLabel": "Metric",
    "userDefinedLabel": "User Defined",
    "hyperLabel": "Hyper",
    "otherLabel": "Other",
    "remarkTips": "Note: Once the service is terminated, remarks and tags will be cleared."
  },
  "dataTraceback": {
    "details": "Details",
    "key": "KEY",
    "value": "VALUE",
    "dataTraceTips": "The data involves combination.",
    "noDataFound": "No data found.",
    "click": "Click",
    "viewAllData": "to view all data."
  },
  "trainingDashboard": {
    "trainingDashboardTitle": "Training Dashboard",
    "calculationChart": "Computational Graph",
    "dataMap": "Data Graph",
    "trainingScalar": "Training Scalar Information",
    "samplingData": "Data Sampling",
    "imagesampleSwitch": "Switch Tag",
    "invalidId": "Invalid training job.",
    "summaryDirPath": "Summary path:",
    "loadingTip": "Loading..."
  },
  "scalar": {
    "titleText": "Scalar",
    "tagSelectTitle": "Tag Selection",
    "xAxisTitle": "Horizontal axis",
    "smoothness": "Smoothness",
    "step": "Step",
    "selectAll": "All",
    "relativeTime": "Relative time",
    "absoluteTime": "Absolute time",
    "fullScreen": "Full Screen",
    "stepBack": "Rollback by Step",
    "openOrCloseSelection": "Enable/Disable Box Selection",
    "toggleYaxisScale": "Switch Y-axis Ratio",
    "charTipHeadName": "Name",
    "charTipTagName": "Tag",
    "charTipHeadValue": "Value",
    "charSmoothedValue": "Smoothness",
    "comparison": "Scalar Synthesis",
    "compareCancel": "Cancel Synthesis",
    "open": "More",
    "close": "Less",
    "invalidData": "Invalid data exists.",
    "restore": "Restore",
    "currentThreshold": "Current threshold",
    "deleteThreshold": "Delete Threshold",
    "setThreshold": "Set Threshold",
    "currentTag": "Current Tag",
    "filterCriteria": "Filter",
    "placeHolderThreshold": "Threshold",
    "or": "Or",
    "and": "And",
    "greaterThan": "Greater than",
    "lessThan": "Less than",
    "applyAllSelectTag": "Apply to Selected Tags",
    "placeHolderNumber": "Enter a number",
    "noSpace": "Do not enter spaces.",
    "sameCompare": "The comparison operator must be unique.",
    "unreasonable": "The logic is improper.",
    "info": "Information",
    "isDelete": "Are you sure you want to delete the current threshold?"
  },
  "images": {
    "titleText": "Image",
    "tagSelectTitle": "Tag Selection",
    "selectAll": "All",
    "open": "More",
    "close": "Less",
    "step": "Step:",
    "setBright": "Brightness",
    "setContrast": "Contrast"
  },
  "histogram": {
    "titleText": "Parameter Distribution",
    "xAxisTitle": "Vertical axis",
    "viewType": "Angle of View",
    "centerValue": "Center Value",
    "step": "Step",
    "relativeTime": "Relative Time",
    "absoluteTime": "Absolute Time",
    "overlay": "Front",
    "offset": "Top",
    "fullScreen": "Full Screen"
  },
  "dataMap": {
    "titleText": "Data Graph"
  },
  "tensors": {
    "titleText": "Tensor",
    "dimension": "Shape:",
    "tensorType": "Data type:",
    "viewTypeTitle": "View",
    "chartViewType": "Table",
    "histogramViewType": "Histogram",
    "tensorDashboardLimitErrorMsg": "The requested data is too large. Go to the tensor page and try another dimension."
  },
  "graph": {
    "titleText": "Computational Graph",
    "downloadPic": "Download",
    "fitScreen": "Fit to Screen",
    "nodeInfo": "Node Information",
    "legend": "Legend",
    "nameSpace": "Namespace",
    "operatorNode": "Operator Node",
    "virtualNode": "Virtual Node",
    "constantNode": "Constant Node",
    "polymetric": "Aggregation Node",
    "dataFlowEdge": "Data Flow Edge",
    "controllDepEdge": "Control Dependency Edge",
    "name": "Name",
    "count": "Subnodes",
    "type": "Type",
    "attr": "Attribute",
    "inputs": "Input",
    "outputs": "Output",
    "outputs_i": "Outputs_i",
    "controlDependencies": "Control Edge",
    "searchLoading": "Locating nodes... Please wait. The locating speed depends on the number of nodes. A large number of nodes will slow down the speed.",
    "queryLoading": "Loading... Please wait.",
    "fullScreen": "Full Screen",
    "tooManyNodes": "Too many nodes to open.",
    "inputNodeName": "Enter node name",
    "guide": "User Guide",
    "guideTitle1": "Introduction 1 of 3: Main Functions",
    "guideTitle2": "Introduction 2 of 3: Node Types",
    "guideTitle3": "Introduction 3 of 3: Edges",
    "guideContent11": "1. In a computational graph display area, you can view a computational graph, zoom in or out a computational graph by scrolling the mouse wheel, and drag a computational graph.",
    "guideContent12": "2. A computational graph can be displayed in full screen or saved as an SVG file.",
    "guideContent13": "3. In the function area on the right, you can switch to view computational graphs of different files or search for nodes in a computational graph.",
    "guideContent14": "4. In the node information, you can click an input or output node to go to the selected node.",
    "guideContent2": "Node types of a computational graph include namespace node, operator node, virtual node,   aggregation node, and constant node. \"Default\" indicates forward propagation, and \"Gradients\" indicates backward propagation. ",
    "guideContent3": "Data edges and control edges exist in a computational graph. A data edge indicates the data input, and a control edge indicates the execution dependency between node.",
    "next": "Next",
    "finish": "Complete",
    "dataTooLarge": "Failed to open the graph because of too many nodes and edges.",
    "tooManyChain": "The direct subnode depth exceeds 70 and cannot be expanded."
  },
  "operator": {
    "titleText": "Profiling",
    "currentCard": "Number of cards",
    "pie": "Pie",
    "bar": "Bar",
    "operatorStatistics": "Operator Statistics",
    "operatorTypeStatistics": "Operator Type Statistics",
    "allOperator": "All",
    "classificationOperator": "Type",
    "card": " ",
    "searchByType": "Enter operator type",
    "searchByName": "Enter operator name"
  },
  "profiling": {
    "profilingDashboard": "Profiling Dashboard",
    "showAverage": "Average value",
    "iterationGapTime": "Step interval",
    "time": "Time",
    "operatorTimeConAnalysis": "Operator Time Consumption Analysis",
    "avgCost": "Average total consumed time:",
    "getCost": "Average data obtaining time:",
    "pushCost": "Average data push time:",
    "lterationGap": "Step Interval",
    "lterationTail": "Step Tail",
    "minddataTitle": "Data Preparation Details",
    "dataQueue": "Data Queues",
    "smartHelper": "Helper",
    "suggestions": "Suggestions",
    "common-profiler_tutorial": {
      "desc": "How Do I Use Profiler for Profiling?",
      "anchor": ["desc"],
      "url": [
        "https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/performance_profiling.html"
      ]
    },
    "step_trace-proposer_type_label": {
      "desc": "Step trace performance optimization"
    },
    "step_trace-iter_interval": {
      "desc": "After the praph mode and dataset sink mode are enabled, if the average step interval is greater than {n1} ms, the process from data processing to computational graph execution can be optimized."
    },
    "common-proposer_type_label": {
      "desc": "Profiling and optimization guide"
    },
    "minddata_pipeline-proposer_type_label": {
      "desc": "Data processing performance optimization"
    },
    "minddata_pipeline-general": {
      "desc": "The {n1} operator in the pipeline may have performance bottlenecks."
    },
    "minddata_pipeline-dataset_op": {
      "desc": "For operator {n1}, you can adjust the num_parallel_workers parameter."
    },
    "minddata_pipeline-generator_op": {
      "desc": "For operator {n1}, you can adjust the num_parallel_workers parameter or optimize the training script. If the performance is not optimized, you can replace the operator with the MindRecordDataset operator."
    },
    "minddata_pipeline-map_op": {
      "desc": "For operator {n1}, you can adjust the num_parallel_workers parameter. If the Python operator is used, you can optimize the training script."
    },
    "minddata_pipeline-batch_op": {
      "desc": "For operator {n1}, you can increase the prefetch_size value."
    },
    "minddata_warning_op": {
      "desc": "Based on the preceding determination, the operator {n1} can be optimized."
    },
    "minddata-proposer_type_label": {
      "desc": "Step interval profiling"
    },
    "minddata_device_queue": {
      "desc": "The ratio of empty queues on a host is {n1}/{n2}, and the ratio of full queues is {n3}/{n4}."
    },
    "minddata_get_next_queue": {
      "desc": "The ratio of empty queues on a chip is {n1}/{n2}."
    },
    "millisecond": "ms",
    "curCard": "Number of cards",
    "stepTrace": "Step Trace",
    "mindData": "Data Preparation",
    "timeLine": "Timeline",
    "rankOfOperator": "Operator Time Consumption Ranking",
    "stepTraceDetail": "Step Trace Details",
    "viewDetail": "Details",
    "stepNum": "Steps",
    "iterGapTimeLabel": "Time",
    "iterGapRateLabel": "Ratio",
    "fpBpTimeLabel": "Time",
    "fpBpRateLabel": "Ratio",
    "tailTimeLabel": "Time",
    "tailRateLabel": "Ratio",
    "operatorDetail": "Operator Details",
    "times": "times",
    "queueStep": "Queue Step Distribution",
    "queueInfo": "Step Interval",
    "pipeline": "Data Processing",
    "pipelineTopTitle": "Average usage of queues between operators",
    "pipelineMiddleTitle": "Queue relationship between operators",
    "deviceQueueOp": "Data Transmission",
    "deviceQueueOpTip": "Data Transmission Operator",
    "getNext": "Data Obtaining Operator",
    "connectorQuene": "Host Queues",
    "getData": "Data Obtaining",
    "opTotalTime": "Total operator execution time:",
    "streamNum": "Number of executed flows:",
    "opNum": "Number of operators:",
    "opTimes": "Total operator execution times:",
    "features": "Functions:",
    "iterationInfo": "The step trace displays the duration of each step from the start of the previous iteration to the end of the step. The main time is divided into three parts: step interval, forward and backward propagation, and step tail.",
    "iterationGapInfo": "Reads data from data queues. If this part takes a long time, you are advised to check the data processing for further analysis.",
    "fpbpTitle": "Forward and Backward Propagation",
    "fpbpInfo": "Executes the forward and backward operators on the network, which carry the main calculation work of a step. If this part takes a long time, you are advised to check the operator statistics or timeline for further analysis.",
    "iterativeTailingTitle": "Step Tail",
    "iterativeTailingInfo": "Performs parameter aggregation and update operations in multi-card scenarios. If the operations take a long time,you are advised to check the time consumed by all_reduce and the parallel status.",
    "statistics": "Statistics:",
    "totalTime": "Total consumed time:",
    "totalSteps": "Total steps:",
    "fpbpTimeRatio": "Ratio of time consumed by forward and backward propagation:",
    "iterationGapTimeRatio": "Ratio of time consumed by step interval:",
    "iterativeTailingTimeRatio": "Ratio of time consumed by step tail:",
    "dataProcess": "This shows the data processing. Data is stored in the host queue during data processing, and then stored in the data queue on a chip during data transmission. Finally, the data transmission operator get_next transmits the data to forward propagation.",
    "dataProcessInfo": "By determining the empty host and data queues, you can preliminarily determine the stage where the performance is abnormal.",
    "analysisOne": "1. If the step interval is long and some batches of the data queue on a chip are empty, the performance is abnormal during data processing and transmission. Otherwise, locate the internal problem of the data transmission operator get_next.",
    "analysisTwo": "2. If the performance is abnormal during data processing and transmission, check the host queue. If the host queue is empty at a high probability, the exception may occur during data transmission.",
    "higherAnalysis": "Note: You can perform advanced analysis based on the time consumed by operators.",
    "chipInfo": "Ratio of empty data queues on a chip:",
    "hostIsEmpty": "Ratio of empty queues on a host:",
    "hostIsFull": "Ratio of full queues on a host:",
    "operatorInfo": "Operator information of {msg1} and {msg2}",
    "workersNum": "Number of threads",
    "queueDeepChartTitle": "{msg} Depth Line Chart",
    "sampleInterval": "Sampling interval",
    "queueTip1": "Ratio of full queues:",
    "queueTip2": "Ratio of empty queues:",
    "totalCapacity": "Total capacity",
    "averageCapacity": "Average used capacity",
    "FPMessage": "FP start operator:",
    "BPMessage": "BP termination operator:",
    "approximateTime": "Total duration ≈ ",
    "stepInputTip": "Steps (an integer ranging from 1 to {max})",
    "inputError": "Input parameter error. Please enter a positive integer ranging from 1 to {max}",
    "defaultTip": "Average value (default)",
    "downloadTimeline": "Download",
    "timelineTips": {
      "title1": "The timeline function helps you analyze the training process and displays the following information:",
      "content11": "- Device(AI CPU or AI core) to which an operator is allocated for execution.",
      "content12": "- Flow tiling policy of MindSpore on the network.",
      "content13": "- Execution sequence and duration of an operator on a device.",
      "title2": "How to view the timeline details?",
      "content21": {
        "part1": "Click ",
        "part2": "Download",
        "part3": " to save a file containing the timeline information to a local host."
      },
      "content22": "View the information using either Google plug-in (chrome://tracing) or Perfetto (https://ui.perfetto.dev/#!/viewer).",
      "content23": {
        "part1": "Select one of the preceding two tools, enter its address in an address box of a browser, and press ",
        "part2": "Enter",
        "part3": ". On the page that is displayed, click ",
        "part4": "Load",
        "part5": " in the upper left corner of the tracing tool or click ",
        "part6": "Open trace file",
        "part7": " in the left pane of the Perfetto tool."
      },
      "title3": "How to use the timeline?",
      "content31": "You can analyze whether the flow tiling policy is proper and whether the step interval and tail time are too long based on the timeline information.",
      "content32": "You can also locate an operator and view and analyze its execution time."
    },
    "unit": "ms/time"
  },
  "hardwareVisual": {
    "processor": "Ascend AI Processor",
    "ram": "Memory",
    "selectedCpu": "Selected CPUs:",
    "allCpu": "Total CPUs:",
    "chipNameTip": "Chip name",
    "deviceIdTip": "Chip ID",
    "availableTip": "Available or not(for reference only)",
    "healthTip": "Chip health index",
    "ipTip": "Chip IP address",
    "aicoreTip": "Chip usage",
    "hbmTip": "Used HBM memory",
    "powerTip": "Chip power consumption",
    "temperatureTip": "Chip temperature",
    "cpuUserTip": "Time for running in user mode (%)",
    "cpuSystemTip": "Time for running in kernel mode (%)",
    "cpuIdleTip": "Idle time (%)",
    "cpuNiceTip": "Time for running low-priority processes (%)",
    "cpuIowaitTip": "Time for waiting for I/O (%)",
    "cpuIrqTip": "Time for processing hardware interrupts (%)",
    "cpuSoftirqTip": "Time for processing software interrupts (%)",
    "cpuStealTip": "Time occupied by other VMs (%)",
    "cpuGuestTip": "Time for running the VM (%)",
    "cpuGuestniceTip": "Time for running low-priority VMs (%)",
    "cpuInterruptTip": "Time for processing hardware interrupts (%)",
    "cpuDpcTip": "Time for remote calling (%)",
    "noNpuInfo": "No Ascend AI processor information",
    "normal": "Normal",
    "generalWarn": "Minor warning",
    "importantWarn": "Major warning",
    "emergencyWarn": "Critical warning",
    "noChip": "The chip does not exist or is not started.",
    "availableFree": "The chip is available.",
    "availableBusy": "The chip is occupied or unavailable.",
    "failQueryChip": "An error occurs during chip information query.",
    "faliQuery": "Query error"
  },
  "components": {
    "summaryTitle": "Training selection",
    "tagSelectTitle": "Tag Selection",
    "selectAll": "All",
    "tagFilterPlaceHolder": "Enter tag (regular expression supported)",
    "open": "More",
    "close": "Less",
    "gridIncorrectDataError": "A maximum of two-dimensionalarrays can be displayed.",
    "gridAccuracy": "Decimal places are reserved.",
    "inCorrectInput": "Invalid input.",
    "gridTableNoData": "No data in the table."
  },
  "error": {
    "50540000": "System error.",
    "50540001": "Incorrect parameter type. Check whether the request parameter types meet the requirements.",
    "50540002": "Incorrect parameter value. Check whether the request parameter values meet the requirements.",
    "50540003": "Mandatory parameters are missing. Check whether all mandatory parameters meet the requirements.",
    "50545001": "The API route resource does not exist.",
    "50545002": "Incorrect HTTP method for requesting the API.",
    "50545005": "The training job does not exist.",
    "50545007": "Loading summary data... Please wait.",
    "50545009": "The queried node is not in the graph. Please refresh.",
    "5054500A": "Failed to decode the URL of the training job ID.",
    "5054500C": "The computational graph does not exist. Please refresh.",
    "5054500D": "The image data does not exist. Please refresh.",
    "5054500E": "The scalar data does not exist. Please refresh.",
    "5054500F": "The parameter distribution data does not exist. Please refresh.",
    "50545010": "The requested data is not in the cache. Refresh.",
    "50542082": "The model name is missing.",
    "50542085": "Invalid model name.",
    "50542215": "Incorrect query parameters.",
    "50542216": "The summary log file is not found.",
    "50542217": "Incorrect summary log path.",
    "50542218": "Incorrect filtering parameter.",
    "50545012": "The tensor data does not exist. Please refresh.",
    "50545013": "The requested data is too large. Try another dimension.",
    "50545014": "The queried tensor data has been replaced by new data. Please refresh.",
    "50548001": "Ascend AI Processor information query timed out."
  }
}
